Contributing Factors to the Continued Blurring of Research and Evaluation: Strategies for Moving Forward
Bibliographic record
Abstract
Despite many studies devoted to the different purposes of evaluation and research, purpose-method incongruence persists. Experimental research designs continue to be inappropriately used to evaluate programs for which sufficient research evidence has accumulated. By using a case example the article highlights several contributing factors to purpose-method incongruence, including the control of the federal level evaluation agenda by researchers, confusion in terminology, and the credible evidence debate. Strategies for addressing these challenges are discussed. Malgre le grand nombre d’etudes portant sur les divers objectifs de l’evaluation et de la recherche, une incongruite persiste au niveau des objectifs/methodes. L’utilisation inappropriee de methodes experimentales de recherches pour l’evaluation de programmes deja abondamment documentes perdure. En presentant un cas, l’article met en lumiere plusieurs facteurs qui contribuent a cette incongruite au niveau des objectifs/methodes, incluant le controle du programme federal d’evaluation par les chercheurs, la confusion terminologique, et le debat des preuves credibles. Des strategies pour s’attaquer a ces defis sont proposees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.799 | 0.765 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.023 | 0.117 |
| Scholarly communication | 0.060 | 0.098 |
| Open science | 0.014 | 0.052 |
| Research integrity | 0.036 | 0.054 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".